Minimal Increase in Total Hip Arthroplasty Surgical Procedural Time with the Use of a Novel Surgical Navigation Tool
Bibliographic record
Abstract
Background: Computer-assisted navigation has proven effective at improving the accuracy of component placement during Total Hip Arthroplasty (THA); however, the material costs, line-of-site issues and potential for significant time increases have limited their widespread use. Objective: The purpose of this study was to investigate the impact of an imageless navigation device on surgical time, when compared with standard mechanical guides. Methods: We retrospectively reviewed prospectively collected data from 61 consecutive primary unilateral THA cases (posterior approach) performed by a single surgeon. Procedural time (incision to closure) for THA performed with (intervention) or without (control) a computer-assisted navigation system was compared. In the intervention group, the additional time associated with the use of the device was recorded. Mean times were compared using independent samples t-tests with statistical significance set a priori at p<0.05. Results: There was no statistically significant difference between procedural time in the intervention and control groups (102.3±28.3 mins vs. 99.1±14.7 mins, p=0.60). The installation and use of the navigation device accounted for an average of 2.9 mins (SD: 1.6) per procedure, of which device-related setup performed prior to skin incision accounted for 1.1 mins (SD: 1.1) and intra-operative tasks accounted for 1.6 mins (SD: 1.2). Conclusion: In this series of 61 consecutive THAs performed by a single surgeon, the set-up and hands-on utilization of a novel surgical navigation tool required an additional 2.9 minutes per case. We suggest that the intraoperative benefits of this novel computer-assisted navigation platform outweigh the minimal operative time spent using this technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".